Observational constraint on the future projection of temperature in winter over the Tibetan Plateau in CMIP6 models

Abstract The Tibetan Plateau (TP) is known as one of the sentinels global climate change. Substantial winter warming over TP will likely lead, directly or indirectly, to a series geological disasters such snow and glacial avalanches. Hence, for better adaptation change, it vital project future change in temperature TP. However, current state-of-the-art models involved sixth phase Coupled Model Intercomparison Project (CMIP6) still produce strong cold biases most parts their historical simulations. On basis selecting optimal models, here we use statistical downscaling method constrain projected CMIP6 models. results show that regions with strongest be near Himalayas densely populated eastern regions. constrained magnitude much greater than ensemble mean original 32 six best these Therefore, early warning forecasting services should strengthened Moreover, long-term spatial varies greatly under four different emission scenarios. Under severe scenario, increase exceeds 10 °C, which destabilize glaciers region, while only 4 °C–6 °C weakest scenario. urgent reduce greenhouse gas emissions control at hotspots vulnerability